Marking a breakthrough in the field of brain-computer interfaces (BCIs), a team of researchers from UC Berkeley and UC San Francisco has unlocked a way to restore naturalistic speech for people with severe paralysis.

This work solves the long-standing challenge of latency in speech neuroprostheses, the time lag between when a subject attempts to speak and when sound is produced. Using recent advances in artificial intelligence-based modeling, the researchers developed a streaming method that synthesizes brain signals into audible speech in near-real time.

As reported in Nature Neuroscience, this technology represents a critical step toward enabling communication for people who have lost the ability to speak.

Artificial intelligence (AI) systems promise transformative advancements, yet their growth has been limited by energy inefficiencies and bottlenecks in data transfer. Researchers at Columbia Engineering have unveiled a groundbreaking solution: a 3D photonic-electronic platform that achieves unprecedented energy efficiency and bandwidth density, paving the way for next-generation AI hardware.

The study, “3D Photonics for Ultra-Low Energy, High Bandwidth-Density Chip Data Links,” led by Keren Bergman, Charles Batchelor Professor of Electrical Engineering, is published in Nature Photonics.

The research details a pioneering method that integrates photonics with advanced complementary-metal-oxide-semiconductor (CMOS) electronics to redefine energy-efficient, high-bandwidth data communication. This innovation addresses critical challenges in data movement, a persistent obstacle to realizing faster and more efficient AI technologies.